Abstract: A method for translating an unstructured existing process into a well-structured process model is provided. The method is executed by a generation engine implemented as a computer program within a computing environment. The method includes analyzing elements or relationships of the unstructured existing process to detect model violations. The model violations include when the elements or the relationships fail at least one model requirement. The method include generating feedback suggestion types corresponding to the model violations and providing the feedback suggestion types in a user interface to automatically resolve the model violations.

Abstract: Systems and methods for automatically modifying a process by an AI (artificial intelligence) agent for adjusting user input of the process are provided. Process data associated with a process is received. The process is automatically modified based on the process data by the AI agent to adjust user input of the process. The modified process is output.

Abstract: Systems and methods for evaluating computer security issues of a computer system are provided. An alert for a computer security issue of a computer system is received. An initial AI (artificial intelligence) agent determines 1) a strategy for evaluating the computer security issue and 2) one or more additional AI agents from a pool of AI agents for evaluating the computer security issue. The computer security issue is evaluated according to the strategy using the one or more additional AI agents. Results of the evaluation of the computer security issue are output.

Abstract: Automation generation, execution validation, and healing by artificial intelligence (AI) agents is disclosed. In order to generate an automation, a user enters a description of the desired task to accomplish into a prompt or some other description and the AI agent attempts to perform the task by employing a screen understanding and document object model (DOM) extraction engine to extract screen content and/or using an object repository. A user may validate the steps that the AI agent will take when it executes before deploying the automation. AI agents may also be used to heal existing robotic process automations (RPA), application programming interface (API) automations, agentic orchestration processes (AOPs), etc. by searching the object repository for solutions for an RPA workflow and then also applying these solutions to other RPA workflows and then also applying the solutions to similar activities of other RPA workflows.

Abstract: Systems and methods for automatically requesting user input for performing a task by an AI (artificial intelligence) agent are provided. The AI agent is executed on a computing system for performing a task. It is determined, by the AI agent, whether to request user input during the performance of the task. In response to determining to request the user input, a particular user to request the user input from is identified by the AI agent. A request for the user input from the particular user is generated by the AI agent based on the task. The request for the user input is sent to the particular user. The performance of the task is continued by the AI agent based on the user input received from the particular user.

Abstract: A training/retraining lifecycle of artificial intelligence (AI)/machine learning (ML) models is managed using workflows with AI flows. Validation may be desired when an AI/ML model is called by an automation executing the workflow, such as an AI agent or robotic process automation (RPA) robot. This validation may include dynamic input from users. The automation may receive the dynamic input from the users and use this data for training a replacement AI/ML model or retraining the called AI/ML model. The state of the workflow may be preserved, both in training and serving. Workflows may be used to keep track of where the current execution is in the ML model lifecycle.

Abstract: Automation generation, execution validation, and healing by artificial intelligence (AI) agents is disclosed. In order to generate an automation, a user enters a description of the desired task to accomplish into a prompt or some other description and the AI agent attempts to perform the task by employing a screen understanding and document object model (DOM) extraction engine to extract screen content and/or using an object repository. A user may validate the steps that the AI agent will take when it executes before deploying the automation. AI agents may also be used to heal existing robotic process automations (RPA), application programming interface (API) automations, agentic orchestration processes (AOPs), etc. by searching the object repository for solutions for an RPA workflow and then also applying these solutions to other RPA workflows and then also applying the solutions to similar activities of other RPA workflows.

Abstract: Frameworks and agentic loops for auto-generating tools and/or building artificial intelligence (AI) agents using tools are disclosed. The agentic loops may be represented in Business Process Model and Notation (BPMN) or any other desired workflow language. The tools can be implemented as static, invocation-specific, or dynamic caller-side or server-side tools. The tools may not be defined at all, may only be abstractly defined, or may be entirely defined within an AI agent definition. The implementation of these tools can be injected as part of the agentic process. The AI agent that implements these tools may also build other AI agents that can leverage the caller-side and server-side tools.

Abstract: Artificial intelligence (AI) agents for user interface (UI) automation to application programming interface (API) automation, and vice versa, are disclosed. The operations performed by a UI automation on the back-end can be captured by an AI agent, including what communications are sent and the context thereof. A robotic process automation (RPA) activity can then be built in an RPA workflow that replaces the UI automation with an API automation. Alternatively, an API automation is replaced with a UI automation. The AI agent employs a fine-tuned generative AI model to understand the context of the communications. The AI agent may also find obsolete duplicates of UI automations and replace these with API automations, or vice versa.

Abstract: Systems and methods for updating an AI (artificial intelligence) agent are provided. Process lifeline data associated with one or more processes is received. Events are identified in the process lifeline data. The process lifeline data and the events are stored in a repository. The process lifeline data and the events are associated with particular processes of the one or more processes. The AI agent is updated based on the process lifeline data, the events, and the associations.

Abstract: A method is provided. The method is executed by a modeling engine implemented as a computer program within a computing environment. The method includes translating a pre-processed model based on an unstructured existing process into a patched process tree. The translating includes detecting, structuring, and translating rigids of the pre-processed model into corresponding process tree equivalent elements of the patched process tree.

Abstract: Generation of unified projects/solutions in artificial intelligence (AI) agent, robotic process automation (RPA) robot, and agentic orchestration process (AOP) development applications is disclosed. Based on a prompt, a process definition document, or some other source, an entire end-to-end business process can be automated, including underlying code and the requisite dependencies. A generative AI model is used to generate an AOP for the business process, including steps performed by AI agent(s) and/or RPA robot(s). The AOP may be represented in Business Process Model and Notation (BPMN), which is an Extensible Markup Language (XML) description of the business process. The designer application may also display a flowchart that shows the AOP. The generative AI model employed by the designer application is fine-tuned to understand which BPMN steps to include in the AOP, including steps that are executed by AI agents and/or RPA robots.

Abstract: Automatic generation of solutions for agentic automation for artificial intelligence (AI) agents, robotic process automation (RPA) robots, agentic orchestration processes (AOPs), and humans-in-the-loop using autopilots are disclosed. Autopilots can be used to generate an AOP for a business process, as well as AI agents and RPA robots for AOP steps used as parts. The AOP defines a business process (e.g., in Business Process Model and Notation (BPMN)). An AOP autopilot may first generate an AOP from a source, such as a prompt, document(s), a spreadsheet, a flowchart, etc. The AOP autopilot may then use AI agent autopilots and RPA autopilots to generate AI agents and RPA workflows for these AOP steps.

Abstract: Automatic generation of solutions for agentic automation for artificial intelligence (AI) agents, robotic process automation (RPA) robots, agentic orchestration processes (AOPs), and humans-in-the-loop using autopilots are disclosed. Autopilots can be used to generate an AOP for a business process, as well as AI agents and RPA robots for AOP steps used as parts. The AOP defines a business process (e.g., in Business Process Model and Notation (BPMN)). An AOP autopilot may first generate an AOP from a source, such as a prompt, document(s), a spreadsheet, a flowchart, etc. The AOP autopilot may then use AI agent autopilots and RPA autopilots to generate AI agents and RPA workflows for these AOP steps.

Abstract: Predictive healing artificial intelligence (AI) agents that preemptively detect impending workflow errors, failures, and/or faults and automatically attempt recovery are disclosed. The healing capabilities of the AI agents may be seamlessly integrated into existing systems and workflows with minimal disruption. A developer may build a user interface (UI) automation workflow that works at design time, but will fail at runtime for some reason. For instance, the automation may be deployed to a computing system with different graphical characteristics than those that were present when the automation was designed, a software update may change aspects of the UI, the computing system may be too busy to run the automation, there may be connectivity issues, a third party system that the automation relies upon may be down, etc. An AI agent can detect that these issues will occur when the automation is run and attempt to automatically correct them.

Abstract: Systems and methods for configuring an RPA (robotic process automation) platform to perform a candidate process automation are provided. Discovery data relating to a candidate process automation is generated. RPA platform design components for configuring an RPA platform to perform the candidate process automation are generated based on the discovery data. The RPA platform design components are presented to a user via a user interface.

Abstract: An agentic extraction method is provided. The agentic extraction method is implemented by artificial intelligence (AI) agents to generate and provide a context grounding for an AI model. The agentic extraction method includes extracting text data from documents including complex document structures and capturing images of the documents. The agentic extraction method includes performing a multistage processing of the text data and the images utilizing large language models to generate an output set including a contextualization and one or more keywords. The agentic extraction method includes converting the output set to vectors including context embeddings to provide the context grounding.

Abstract: An agentic searching method is provided. The agentic searching method is implemented by artificial intelligence (AI) agents. The agentic searching method includes performing advanced agentic searching of a sematic storage including context embeddings in response to a complex query. The agentic searching method includes outputting a context grounding resulting from the searching of the sematic storage and the context embeddings to an AI model with the complex query to enhance an accuracy of a response of the AI model. The agentic searching method includes receiving the response to the complex query from the AI model.

Abstract: An agentic memory method is provided. The agentic memory method is implemented by artificial intelligence (AI) agents. The agentic memory method includes storing vectors including context embeddings generated by advanced agentic extraction in response to queries in an agentic memory. The agentic memory method includes receiving and matching a complex query to the vectors of the agentic memory by an advanced agentic search to determine a context grounding. The agentic memory method includes providing the context grounding to an AI model with the complex query to enhance an accuracy of a response of the AI model and receiving the response to the complex query from the AI model.

Abstract: A system and method provide an integrated automation solution that links multiple systems and applications of a contact center operation and provides a unified support interface and unified knowledge base that delivers relevant data in real-time to assist contact center personnel during a customer interaction. Robotic Process Automation (RPA) is used for automating workflows and processes with robots (e.g., attended and/or unattended) that perform various tasks and activities for capturing information (data, documents, etc.) from multiple front-end and/or back-end systems and applications to provide the necessary data and information in real-time during a contact center session.